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Google Generative-AI-Leader Prüfungsplan: Thema Einzelheiten Thema 1 Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
Thema 2 Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Thema 3 Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Thema 4 Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Generative-AI-Leader Pruefungssimulationen <<
Sie können so einfach wie möglich – Generative-AI-Leader bestehen! Generative-AI-Leader ist eine der Google Zertifizierungsprüfungen. IT-Fachmann mit Google Zertifikat sind sehr beliebt in der IT-Branche. Deshalb legen imme mehr Leute die Generative-AI-Leader Zertifizierungsprüfung. Jedoch ist es nicht so einfach, die Google Generative-AI-Leader Zertifizierungsprüfung zu bestehen. Wenn Sie nicht an den entprechenden Kursen teilnehmen, brauchen Sie viel Zeit und Energie, sich auf die Prüfung vorzubereiten. Nun kann DeutschPrüfung Ihnen viel Zeit und Energie ersparen.
Google Cloud Certified – Generative AI Leader Exam Generative-AI-Leader Prüfungsfragen mit Lösungen (Q22-Q27): 22. Frage A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
A. Overfitting B. Hallucination C. Data dependency D. Edge case Antwort: C
Begründung: The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles. This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups. Hallucination © is the invention of facts or data. Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics. Bias is the result of the data dependency, not the fundamental limitation itself. (Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
- Frage A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions: “To address this customer's problem, we should first identify the core issue they are experiencing. Then, we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer's message: 'My order hasn't arrived, and the tracking number shows no updates for a week,' what should be the next step in resolving this?” What type of prompting is this?
A. Role-based B. Zero-shot C. Few-shot D. Chain-of-thought Antwort: D
Begründung: The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: “first identify,” “then check,” “if a solution exists, explain,” “if not, escalate.” This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis. Zero-shot (A) would be the raw question without any structure. Few-shot (B) would involve providing examples of successfully solved problems. Role-based © would involve assigning a persona (e.g., “Act as a customer service expert”) but would not explicitly mandate the sequential process. The inclusion of the explicit steps (“first identify,” “then check,” etc.) is the defining characteristic of Chain-of-Thought prompting. (Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)
- Frage A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?
A. Use gen AI to identify potential bugs and security vulnerabilities in their code. B. Use gen AI to refactor and optimize existing code. C. Use gen AI to automatically generate comprehensive documentation for their code. D. Use gen AI to suggest code snippets and complete functions. Antwort: D
Begründung: While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping. ________________________________________
- Frage A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
A. Focusing on collecting only quantitative feedback data in future surveys. B. Applying data anonymization techniques to remove or obscure sensitive data. C. Implementing strong access controls to limit which teams can view the raw survey data. D. Ensuring that the AI model is trained on a large and diverse dataset. Antwort: B
Begründung: The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model. According to Google's Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification. Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model's parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application's outputs. This is a crucial early step in the ML lifecycle for datasets containing sensitive information. Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn't solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation. (Reference: Google Cloud's Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)
- Frage What is a key advantage of using Google's custom-designed TPUs?
A. TPUs are lightweight processors intended for deployment on edge devices. B. TPUs are specialized AI processors that excel at parallel processing for machine learning workloads. C. TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud. D. TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers. Antwort: B
Begründung: TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs). TPUs excel at parallel processing © for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini). TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources. While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models. Options A describes the Edge TPU or Gemini Nano deployment strategy, not the general, key advantage. Options B and D misrepresent the function, as TPUs are compute hardware, not storage accelerators or general-purpose CPU replacements. (Reference: Google's training materials on the Generative AI Infrastructure Layer explicitly list TPUs and GPUs as the physical hardware components providing the core computing resources needed for generative AI, with TPUs being specialized for accelerating ML workloads and parallel processing.)
- Frage ......
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